2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)最新文献

Deep Reinforcement Learning for the Capacitated Vehicle Routing Problem with Soft Time Window 带软时间窗的有能力车辆路径问题的深度强化学习

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2022-11-01 DOI: 10.1109/WCSP55476.2022.10039414

Xiaohe Wang, Xinli Shi

引用次数: 0

Modulation Recognition Method of MAPSK Signal MAPSK信号的调制识别方法

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2021-01-01 DOI: 10.1109/WCSP52459.2021.9613669

Yingwen Liu, Jing Xu

引用次数: 0

Artificial Intelligence Routing Method in Wireless Sensor Network for Sewage Treatment Monitoring 污水处理监测无线传感器网络中的人工智能路由方法

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2021-01-01 DOI: 10.1109/WCSP52459.2021.9613299

Fang-Mei Zhou, Zhonglou Lu, Zhifeng Zhu, T. Cheng

引用次数: 0

Integrated Interference Solutions Between 5G and Satellite Systems 5G与卫星系统集成干扰解决方案

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2021-01-01 DOI: 10.1109/WCSP52459.2021.9613464

T. Cheng, Fang Zhou

引用次数: 0

Electromagnetically Induced Transparency in a Coupled NV Spin-Mechanical Resonator System 耦合NV自旋-机械谐振器系统的电磁感应透明

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2021-01-01 DOI: 10.1109/WCSP52459.2021.9613356

Q. Liao, Xiaoqian Wang, Gaoqian He

引用次数: 0

A Data Protection-Oriented Design Procedure for a Federated Learning Framework 面向数据保护的联邦学习框架设计过程

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2020-10-21 DOI: 10.1109/WCSP49889.2020.9299730

Yue Yu, Ming-Quan Zeng, Zhijie Qiu, Lei Luo, Hong Chen

{"title":"A Data Protection-Oriented Design Procedure for a Federated Learning Framework","authors":"Yue Yu, Ming-Quan Zeng, Zhijie Qiu, Lei Luo, Hong Chen","doi":"10.1109/WCSP49889.2020.9299730","DOIUrl":"https://doi.org/10.1109/WCSP49889.2020.9299730","url":null,"abstract":"Artificial intelligence is a bellwether of today’s new technological revolution. As a branch of artificial intelligence, machine learning faces two main problems in practical application: 1) data owned by most enterprises are difficult to aggregate; and, 2) big data owners pay more and more attention to data privacy and security, which leads to the problem of data island. Federated learning (FL), as a distributed FL paradigm, which can enable all parties to achieve the purpose of co-building models while ensuring data privacy and exposure, provides a possible solution to the mentioned problems of machine learning. The FL takes full advantage of participants’ data and computing power and builds a more robust machine learning model without sharing the data. In an environment with strict data regulation, the FL can effectively solve the key problems, such as those related to data privacy and data rights. However, most of the existing FL-based frameworks do not pay attention to the impact of data source distribution on FL training. Therefore, this paper proposed a data-oriented FL framework called the Federated AI Engine(FAE), which can solve FL problems without leaving the data in control. The proposed framework provides a method that can be used to verify FL quickly for researchers who intend to try federal learning.","PeriodicalId":6858,"journal":{"name":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","volume":"43 1","pages":"968-974"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80109885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Low Complexity Iterative Combining-Equalization Algorithm Based on SIC 基于SIC的低复杂度迭代组合均衡算法

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2019-01-01 DOI: 10.1109/WCSP.2019.8928039

Mei Gao, Zhongwei Liu, Feng Jing, Haodan Ran, Shuyin Zhang

引用次数: 0

Security Enhancement In Bitcoin Protocol 比特币协议中的安全增强

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2018-03-22 DOI: 10.1109/WISPNET.2018.8538578

R. Bala, R. Manoharan

{"title":"Security Enhancement In Bitcoin Protocol","authors":"R. Bala, R. Manoharan","doi":"10.1109/WISPNET.2018.8538578","DOIUrl":"https://doi.org/10.1109/WISPNET.2018.8538578","url":null,"abstract":"The radical growth of Bitcoin revolutionized the field of digital currencies by offering a secure replacement for online electronic payments like card payments and Internet banking. Notably, Bitcoin has become popular since it maintains the anonymity of the users and enables peer-to-peer instant payments across the world. It is a decentralized money transfer protocol that does transactions without exposing user credentials and does verifications without any third-party intervention. Though it maintains anonymity, decentralization concept introduced vulnerabilities leading to severe security issues like double spending, block withholding and 51% attacks. These issues necessitate security specific enhancements in Bitcoin protocol. Therefore, this paper proposes a method of incorporating criteria check for miners to participate in mining process and a verification process to join the mining pool. The proposed idea mitigates double spending, block withholding, and 51% attacks. Further, a new approach is proposed namely, refining block creation and verification strategy for improving transaction rate without compromising security.","PeriodicalId":6858,"journal":{"name":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","volume":"8 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75251208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Effect of Modulation Techniques on the Performance of Voice and Video Service for WiMAX Networks in Baghdad 调制技术对巴格达地区WiMAX网络话视频业务性能的影响

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2018-03-22 DOI: 10.1109/WISPNET.2018.8538693

H. M. T. Al-Hilfi, Shatha k. Jasim Al hummadi, M. Mustafa

引用次数: 1

Reinforcement Learning Based Decision Fusion Scheme for Cooperative Spectrum Sensing in Cognitive Radios 基于强化学习的认知无线电协同频谱感知决策融合方案

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Pub Date : 2018-03-22 DOI: 10.1109/WISPNET.2018.8538528

V. Balaji

{"title":"Reinforcement Learning Based Decision Fusion Scheme for Cooperative Spectrum Sensing in Cognitive Radios","authors":"V. Balaji","doi":"10.1109/WISPNET.2018.8538528","DOIUrl":"https://doi.org/10.1109/WISPNET.2018.8538528","url":null,"abstract":"Spectrum Sensing is a key module in Cognitive Radios (CR) for detecting spectrum holes. The performance of spectrum sensing algorithms is compromised due to channel impairments, such as, multi-path fading and shadowing. Cooperative Spectrum Sensing (CSS) scheme mitigates the above issues and improves the spatial diversity gain of Secondary Users (SUs). In this paper, we present Reinforcement Learning (RL) based CSS scheme with the objective of improving cooperative sensing accuracy by maximizing expected cumulative reward. Using reinforcement learning, the Fusion Center(FC) makes a global decision by interacting with the radio environment which consists of cooperative SUs and primary transmitter. The cooperative SUs are deployed randomly in a fading wireless channel environment modeled as a Markov Decision Process (MDP). The optimal solution of RL based CSS algorithm is formulated using policy iteration to meet the requirements of IEEE 802.22 Wireless Regional Area Network (WRAN) standard. The simulation results show that the RL based CSS scheme improves the detection performance under channel fading/shadowing and overall cooperative learning capability.","PeriodicalId":6858,"journal":{"name":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","volume":"20 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78472924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3